real-time monitoring of trace gas concentrations in syngas
TRANSCRIPT
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Real-Time Monitoring of Trace Gas Concentrationsin Syngas
J. Herbig1, R. Gutmann1, K. Winkler1*, A. Hansel1,2 and G. Sprachmann3
1 Ionimed Analytik GmbH, Eduard-Bodem-Gasse 3, 6020 Innsbruck - Austria2 Institute of Ion Physics and Applied Physics, Leopold-Franzens University Innsbruck, Technikerstrasse 25, 6020 Innsbruck - Austria
3 Shell Global Solutions International BV, Researcher Gas Separation Technologies, Amsterdam - The Netherlandse-mail: [email protected] - [email protected] - [email protected]
[email protected] [email protected]
* Corresponding author
Resume — Suivi en temps reel des concentrations de gaz traceurs dans un extrait de gaz de synthese—
Un spectrometre de masse par reaction de transfert de protons (PTR-MS, Proton Transfer
Reaction – Mass Spectrometer) a ete utilise pour l’analyse de gaz de synthese dans un procede
industriel Fischer-Tropsch. Un PTR-MS peut detecter une grande variete de composes organiques
et inorganiques volatils en temps reel et avec une sensibilite elevee. Associe a un multiplexeur, il
permet un suivi en ligne (en temps reel) des contaminants a l’etat de traces a differents stades d’un
procede Fischer-Tropsch. Plusieurs composes volatils, tels que HCN, H2S, RSH, des carbonyles,
des acides, des alcools et autres, ont ete mesures dans du gaz de synthese. Cet article decrit la
configuration pour le suivi du gaz de synthese en utilisant le PTR-MS et resume le resultat de ce
projet de preuve de concept.
Abstract — Real-Time Monitoring of Trace Gas Concentrations in Syngas — A Proton Transfer
Reaction Mass Spectrometer (PTR-MS) was used for the analysis of syngas in an industrial
Fischer-Tropsch process. A PTR-MS can detect a variety of volatile organic and inorganic com-
pounds in real-time and with high sensitivity. Together with a multiplexer, this allows for online
(real-time) monitoring of the trace contaminations at different stages of a Fischer-Tropsch process.
Several volatile compounds, such as HCN, H2S, RSH, carbonyls, acids, alcohols and others have
been measured in syngas. This paper describes the setup to monitor syngas using PTR-MS and sum-
marizes the result of this proof-of-principle project.
Oil & Gas Science and Technology – Rev. IFP Energies nouvelles, Vol. 69 (2014), No. 2, pp. 363-372Copyright � 2013, IFP Energies nouvellesDOI: 10.2516/ogst/2012083
INTRODUCTION
In industrial gas processes the knowledge about the
exact composition of the gases is important and is closely
monitored and controlled. Beside the main constituents
which typically appear in the percent concentrations,
also trace gas impurities at much lower concentrations
can play a crucial role. For example, silico-organic
compounds are converted to silicon oxide in gas combus-
tion, which result in an increased maintenance [1] and
potentially in the deactivation of an exhaust gas catalytic
converter. Another example, the catalytic activity of
most transition metals is drastically reduced by the pres-
ence of sulfur-containing compounds at extremely low
concentration. This poisoning effect is a major problem
in many catalytic reactions, especially hydrogen reac-
tions such as methanation of coal synthesis gas or
reforming of naphthas [2]. Furthermore, when large
amounts of off-gas are being released, also trace concen-
trations of compounds of extreme toxicity constitute an
environmental risk.
A Proton Transfer Reaction Mass Spectrometer
(PTR-MS) [3, 4] is a gas analytical device for the sensi-
tive detection of volatile organic compounds. These
devices are well established in several fields of research
where their capability to analyze samples in real-time
yields insights into the dynamic of the system. Prime
examples where PTR-MS technique is extensively used
for VOC analysis are environmental [5, 6] food and fla-
vour [7, 8] and medical [9, 10] applications. Recently, this
method is extended to monitor drugs and explo-
sives [11, 12]. Broad application for industrial process
monitoring however is not common so far [13-15].
Moreover, the analysis of sample gas via PTR-MS is pre-
dominantly focussed on the analysis of volatile organic
compounds in a matrix of air.
The objective of this project was the real-time
monitoring of trace gas compounds in syngas at dif-
ferent process steps. In this paper, we demonstrate
the extension of the PTR-technique to the monitoring
of trace compounds in an industrial Fischer-Tropsch
gas process. This is new in several ways. First, the
matrix consists of syngas (mixture of 30% H2 and
70% of CO) [16]. Second, beside some organic com-
pounds, there is a high interest in the monitoring of
inorganic and organo-metallic trace compounds in
syngas. Finally, the gas composition of several steps
in the process have to be monitored in parallel and
in an automated fashion, thus a multiplexer was inte-
grated into the setup.
We have employed PTR-MS instruments in several
installations in a Fischer-Tropsch process operated
by Shell Global Solutions (SGS) to monitor trace
concentrations in syngas. In this paper, we describe this
application and give an overview over the main volatile
organic and inorganic compounds that can be observed
and the concentration ranges that can be expected. In
many PTR-MS applications, mainly organic compounds
such as methanol, acetone or benzene, are measured rou-
tinely, whereas the measurement of compounds, such as
hydrogen sulfide, methyl-mercaptan, metal carbonyls,
has not been reported so far. Here, we will summarize
the optimization of the measurement of the main com-
pounds. We have performed calibrations, which allow
a determination of the sensitivity for specific com-
pounds. We assess also the limits of detection of setup
and instrument and discuss the restrictions and potential
improvements.
1 MATERIALS AND METHODS
The heart of the experiment was a Proton Transfer Reac-
tion Mass Spectrometer (PTR-MS) system, which was
installed in an industrial gas processing site for continu-
ous monitoring of trace gas compounds in different pro-
cessing steps.
In standard applications the PTR-MS sample gas inlet
is directly connected to the gas stream. For the present
application several modifications were necessary. Both
main constituents of syngas (CO 70% and H2 30%) pose
substantial (health) risks. Therefore, the PTR-MS was
connected to a closed gas system, i.e. the exhaust of
the PTR-MS has to be connected to the off-gas stream
of the installation, see Figure 1. Unless otherwise
stated, all sample gas carrying lines are treated with
SilconertTM2000 [17] to make them inert in order to
avoid surface adsorption.
12345
Dilution gas (N2) N2
Dilution unitStream selector
Syngas
Offgas
PTR-MSFC1
FC215 sccm 100 sccm
Figure 1
Schematic of the setup for the monitoring of process gas.
Different streams can be selected by the stream selector.
Typically, in the dilution unit 15 sccm of the process gas
were diluted with 100 sccm of N2. Approximately 80 sccm
of the diluted gas were drawn into and analyzed by the
PTR-MS.
364 Oil & Gas Science and Technology – Rev. IFP Energies nouvelles, Vol. 69 (2014), No. 2
1.1 PTR-MS
We have used a high-sensitivity PTR-MS system
(Ionicon Analytik GmbH, Innsbruck, Austria). The prin-
ciples of operation of PTR-MS are extensively described
elsewhere [3], specifications are listed in [18].
The PTR-MS has been slightly modified: in order to
ensure the tightness of the system even in the event of
back-pressure from the off-gas line, the exhaust water
trap has been removed. To efficiently prevent condensa-
tion of water (from sample gas or the ion source) in the
pump or exhaust, dry nitrogen (0.5-1 L/min) has been
used.
Initially, we have performed scans ranging from
m/z 20 Th (ion mass-to-charge ratio)1 to m/z 200 Th in
order to screen for significant signals. For the continu-
ous monitoring, we have selected several m/z (MID
mode), which were composed of the compounds of
interest, their isotopes and potentially fragments and
other signals that appeared significant in the scans. The
typical dwell time per m/z was set to 1 s.
The PTR-MS was operated at the following parame-
ters: drift pressure of 2.2 mbar and a drift voltage of
400 V. After initial tests with different drift voltages, this
voltage was chosen for good sensitivity for several com-
pounds and little fragmentation. The temperature of the
reaction chamber was kept at 80�C. The sample gas inlet
line connecting the PTR-MS and the dilution unit was
also heated to 80�C, although everything else upstream
was at room temperature. In this application, the sample
gas dew point was held at 20�C with a pressure of
2 bar (a) before it enters into theFischer-Tropschprocess.
1.1.1 Dilution
By using a sample gas dilution system, the range for lin-
ear signal response in the PTR-MS (normally � 10 ppt
to 10 ppm) can be shifted upwards to optimize for the
current conditions. In the dilution unit, see Figure 1, a
mass flow controller (FC1) restricts the process gas flow
to 15 sccm (standard cubic centimeter per minute). These
15 sccm were diluted with 100 sccm of clean N2 (con-
trolled by FC2), resulting in a dilution ratio of 15:100
(9 6.67). The PTR-MS draws about 80 sccm of the gas
mix, the excess gas goes into the off-gas line. The dilution
was necessary for several reasons. First, high concentra-
tions of some compounds were observed in several of the
streams, which were close to the upper limit of linearity
of the PTR-MS. If this limit is reached for one com-
pound, the linearity of the measured signal of all other
compounds would also be compromised. Second, the
composition of the sample gas matrix can have a signif-
icant influence on the kinetics of the ionization reaction.
A gas matrix consisting mainly of N2 ensures constant
conditions and the comparability to other measure-
ments, e.g. calibrations where N2 was used as a dilution
gas. Finally, through dilution, the high H2 concentration
can be lowered to reduce the risk of explosion and to
avoid a depreciation of the desired vacuum in the instru-
ments detection region, due to the lower pumping effi-
ciency of the turbo molecular pumps for hydrogen.
The dilution was accomplished with an adapted version
of the ionimed Gas Calibration Unit (GCU) [19]. This
had the advantage that the dilution could be controlled
by software.
1.1.2 Data Normalization
All data have been processed according to standard prac-
tice inPTR-MSmeasurements, i.e. the rawdatahavebeen
normalized to 1 million cps (counts-per-second) of pri-
mary ions (H3O+, actual number of primary ions
� 20 million cps), resulting in instrument-independent
“normalized counts-per-second” (ncps). The instruments
sensitivity, determined in calibration experiments,
has been used to convert measured signals in units of
parts-per-billion by volume (ppbv). Therefore a correc-
tion for the instruments transmission function was not
necessary. As a final step, the dilution factor has been
accounted for to get the undiluted concentrations in the
gas streams.
1.1.3 Stream Selector
APTR-MS constantly draws and analyzes sample gas. In
order to measure at several points in the process in paral-
lel, the inlet sample gas streamhas to bemultiplexed. This
means that several streams (each drawn from a certain
point in the process) have to be alternately connected to
the PTR-MS’s inlet, which poses new challenges. When
streams are switched, an instantaneous change of concen-
trations is expected and therefore the shared part of the
sampling line needs to consist of inert material in order
to minimize carry-over effects. Also, when switching to
a sampling line, the flow and pressure in this line should
not be changed. For switching, we have used a multiplex-
ing valve (stream selector, in Fig. 1) that was already
installed in the process. This device has four ports, of
which three are connected to different points in the pro-
cess, see below, and one to calibration gas standards. In
the following, we indicate the streams by numbers, indi-
cating their position in the process, i.e. stream 4 is down-
stream of stream 3, and so forth.
1 Due to the proton-transfer reaction, the charge is always 1 and the
mass of the ion (protonated mass) is always 1 u higher than that of the
compound. The m/z is usually given in Th (Thomson), which is
sometimes omitted.
J. Herbig et al. / Real-Time Monitoring of Trace Gas Concentrations in Syngas 365
1.1.4 Data Selection
In the simple setup, the stream selector was programmed
to switch between the different sampling streams on a
periodic basis (every hour) while the PTR-MS measured
continuously. In this setup, an additional manual valve
was operated to alternate between streams 1 and 2.
As can be seen from Figure 2, the switching between
streams is clearly visible in the signals. By comparing
to the sequence of the stream selector, we could
attribute every data block (between switches) to the cor-
responding stream. Data points taken immediately after
switching were omitted to account for carry-over effects.
The visible carry-over-effect we mainly attributed to the
shared part of the sample line and most importantly to
the inner surface of the flow controller – the only party
consisting of uncoated stainless steel. The remaining
data (last 20 data points) in one block have been aver-
aged to represent one data point.
We have performed regular measurements of the
background in the pure N2 dilution gas. This back-
ground (typically low) has been subtracted. In the case
of overlapping signals (see Sect. 2.3), we have performed
additional corrections.
1.1.5 Calibration
In principle, in PTR-MS measurements the sensitivity can
be calculated since the underlying mechanism is simple:
when a compound has a proton affinity higher than that
of H2O, i.e. > 165 kcal/mol (691 kJ/mol), then ionization
viaproton transfer happens in the reaction chamber at every
collision with a precursor ion (H3O+) [3]. Typically, this
method delivers results with an accuracy of better than
50%. Not included in this simple model are different reac-
tion kinetics or fragmentation of a compound. In contrast,
in a calibration experiment all necessary parameters are
included.Wehave therefore calibrated the PTR-MS system
for most of the compounds of interest, such as H2S, HCN,
Fe- and Ni-carbonyl, and a series of VOC (volatile organic
compounds), see below.
In order to calibrate the instruments, calibration gas
standards were connected to the stream selector. Using
the flow controllers in the dilution unit different mixing
ratios could be set in order to get a calibration curve. We
have performed multi-point calibrations or “quick”
calibrations, which consist of only two steps: a
background measurement and one mixing ratio at
moderate concentrations. This already delivers suffi-
ciently accurate results, since the signal response of the
PTR-MS is linear over several orders of magnitude.
The following gas standards were used: a multi com-
ponent gas standard (Apel Riemer Inc., USA, custom
made VOC mix) containing 17 VOC with approximately
1 ppm in N2 of methanol, acetonitrile, acetaldehyde, eth-
anol, acroleine, acetone, benzene and others. For the
compounds Fe(CO)5, Ni(CO)4, HCN and H2S, several
separate standards (Linde gas) in different concentra-
tions were used. Typically the accuracy of the concentra-
tions was 5%.
1.1.6 Limit of Detection (LOD)
The LOD is the signal intensity, which is significantly
(3r) above the background noise level. The background
noise can be determined in blank measurements, which
were performed on a regular basis. In the employed sys-
tem, we have several sources for background noise:
– electrical background (in PTR-MS detector dark
counts),
– chemical noise (e.g. contaminations in the N2 dilution
gas),
– cross-talk between streams (insufficient equilibration
after switching),
– overlap on the mass of interest from other com-
pounds.
Typically PTR-MS instruments have very low inher-
ent electronic and chemical background. The largest
background in a blank measurement in this setup is
given (for most compounds) by the chemical back-
ground: either remnant contaminations in the gas line
(cross-talk) or contamination in the N2 gas used for dilu-
tion. As an example, in Nitrogen 6.0 there can be still
100 ppb of hydrocarbons. Thus, it makes sense to differ-
entiate different LOD.
Method LOD
This LOD has to be determined experimentally. It is
certainly an upper limit for the LOD. It includes all
1 000 000
100 000
10 000
1000
100
10
1
271
253
235
217
199
181
163
145
127
10991735537191
197
61493533
79
Time (min)
Raw
sig
nal (
cps)
1 2 3 1 4
Figure 2
Typical raw signal data measured with the PTR-MS. The
streams have been identified with the sequence of the
stream selector.
366 Oil & Gas Science and Technology – Rev. IFP Energies nouvelles, Vol. 69 (2014), No. 2
possible noise sources and reflects the whole setup, lin-
ing system, flow controller, cross-talk after the stream
selector and the (long term) background variation over
the whole course of the experiment. Also the dilution
factor and data evaluation steps are included in this
LOD.
Instrument LOD
The measured instrument LOD has been determined in
the calibration measurements and still includes the
chemical noise in the dilution gas. In contrast to the
method LOD, the blank sample has been measured with
longer flushing times of the lines.
Achievable LOD
We also estimate the LOD that would be possible in an
ideal setup, i.e. in the absence chemical background
noise, without dilution and with long integration time.
This LOD only includes the measured electronic noise
of the instrument and the measured sensitivity (for
10 seconds integration time).
2 RESULTS AND DISCUSSION
In a first step, we have performed repeated scans over all
gas streams to determine where significant signals are to
be expected. From over 200 measured m/z, we find
approx. 123 m/z for which the highest observed concen-
tration (maximum over all gas streams) where signifi-
cantly above the background and at least above
� 50 ppt. From these, we have selected several com-
pounds of interest, which are presented in Table 1.
2.1 Calibration and LOD
A typical calibration plot is depicted in Figure 3. The
PTR-MS ncps-signal is plotted versus the concentration,
which has been calculated taking the original concentra-
tion in the employed gas standard and taking into
account the flows of the mass-flow controllers FC1 and
FC2 from Figure 1.
The slope of the calibration plot is the sensitivity of
the PTR-MS for this compound and is used to convert
measured data to concentrations. The data measured
at 0 ppb, gives the instrumental background (usually
low) and the noise in this data determines the LOD, as
described above. Table 2 summarizes sensitivities and
LOD of relevant compounds. The sensitivity depends
on the instrumental settings, which were kept constant
throughout all measurements. Also, the gas matrix can
have an influence [20]. In the calibration experiments,
the matrix was pure N2. However, we do not expect a
large deviation to the experimental conditions, since
TABLE 1
Listed are m/z with prominent signal intensities, mostly we give the
tentatively identified compound, typically backed-up by the isotopic
ratio. The average concentration ranges observed (over time and over
sampling points) are represented by logarithmic categories ranging
from o (< 0.3 ppb), oo (< 3 ppb), ooo (< 30 ppb),
to ooooo (> 300 ppb range)
m/z (Th) Concentration
range
Potential compound
28 o HCN
33 ooooo Methanol
35 ooo H2S
42 o Acetonitrile
43 oo Acylium fragment
(from e.g.
isopropanol)
47 oo Formic acid
49 ooo Methyl mercaptan
51 ooo Methanol water
cluster
57 o Acroleine
59 o Acetone
61 ooo Acetic acid
105 oo Ni(CO)H3O + (main
fragment of Ni
(CO)3H+)
143 oo Ni(CO)3H+
197 oo Fe(CO)5H+
Slope y = 7,5289xR2 value = 0,9953
0
2
4
6
8
10
12
14
0 400 800 1 200 1 600
Sig
nal (
x 10
00 n
cps)
HCN concentration (ppb)
Calibration HCN m/z 28
Figure 3
A typical calibration curve. As example HCN is shown.
J. Herbig et al. / Real-Time Monitoring of Trace Gas Concentrations in Syngas 367
the matrix still consists mainly of N2 (87%), with CO
(9%) and H2 (4%). Moreover, sample gas humidity
can have an influence on some exceptional compounds,
with HCN and H2S being two. Through the control of
the dew-point and the dilution with dry nitrogen, this
influence is negligible.
2.2 Discussion HCN Measurement
HCN is a “sticky” compound, i.e. it shows strong surface
adsorption effects. When a high concentration of HCN
has been transported in a gas line, it is adsorbed by all
wetted surfaces in this system. When the time for
equilibration is too short, a change from a low to a high
concentration leads therefore to slightly underestimated
values, which is relatively insignificant. However, a step
from a high concentration to a low concentration (like in
blank measurements) results in a relatively high back-
ground.
We have estimated the timescale for equilibration of
HCN when switching from streams with high concentra-
tions (1.5 ppm) to one with virtually no HCN. We
observe, that the concentration slowly “decays” towards
the background concentration. With this setup, we
observe decay with two time constants: a fast decay
directly after switching fromahigh to a low concentration
with a half-life time of only a few minutes. After several
minutes, the decay becomes slower. Assuming an
exponential model for those points taken after
25 minutes, we obtain a half-life time of 35 minutes. This
adds up to 2 hours for the signal to decay from a high
concentration (1.5 ppm) to the background (65 ppt).
Therefore an insufficient equilibration after switching
leads to an unnecessarily high method LOD. This leaves
large room for optimization of the sampling setup, dis-
cussed below.
2.3 Discussion H2S Measurement (Methanol Influence)
Another discrepancy is found in the LOD for H2S.
The reason for this is due to interference between
different m/z. We observe generally high concentrations
of methanol. The protonated mass of methanol is
m/z 33.0 Th. H2S is measured on protonated mass
m/z 35.0 Th. Due to the naturally occurring isotopes,
a small fraction of the methanol signal also shows up
on m/z 35.0 Th. This means that a methanol signal of
2 000 ppb gives a false signal equivalent to 15 ppb of
H2S. Natural isotopic ratios are well known and can be
used for identification of compounds or equivalently to
correct for this overlap.
Using the natural isotopic distribution, see Table 3, the
contribution of methanol to the H2S signal can be suffi-
ciently corrected for an accurate measurement of H2S.
TABLE 2
Summary of sensitivities and LOD. The dilution has been included in all listed LOD. The LOD without dilution would be a factor of 6.7 lower
Compound
(formula)
m/z (Th) Sensitivity
(ncps/ppb)
Method LOD
(ppb)
Instrument LOD
(ppb)
Achievable LOD
(10 s) (ppb)
Hydrogen cyanide
(HCN)
28 7.5 1.36** 0.06 0.037
Methanol
(CH4O)
33 15.6 4.70 0.34 0.018
Hydrogen sulfide
(H2S)
35 4.1 3.20** 1.67 0.068
Methyl mercaptan
(CH4S)
49 4.0* 1.25 0.71 0.070
Nickel carbonyl
(Ni(CO)4)
105 4.7 0.53 0.23 0.059
Iron carbonyl
(Fe(CO)5)
197 8.1 0.93 0.10 0.034
Acetone
(C3H6O)
59 18.0 0.50 0.23 0.015
Acetic acid
(C2H4O2)
61 8.0* 1.04 0.58 0.035
* Sensitivity estimated by proxy, ** see respective discussion HCN, H2S measurement.
368 Oil & Gas Science and Technology – Rev. IFP Energies nouvelles, Vol. 69 (2014), No. 2
The residual error is typically negligible. This can be seen
in Figure 4, where the methanol signal is displayed with
the corrected and uncorrected H2S signal. However,
when a very low (i.e. the background) concentration of
H2S is to be measured, a residual error in methanol
correction leads a large relative error. This contributes
significantly to the noise level in the H2S background
and therefore deteriorates the method LOD. In PTR-
instruments with a high resolution time-of-flight mass
detector such interferences are averted [21].
2.4 Stability
From the measured data, we can also evaluate the stabil-
ity of the system. The short term stability of the system
can be estimated from the measurement of a stable con-
centration, e.g. a step in a calibration experiment. We
have chosen the HCN calibration as a worst-case
example, since the HCN signal shows strong surface
effects and therefore drifts in the data. Table 4 shows
mean concentrations of HCN at different calibrations
steps and the relative standard deviation at each
step.
Long Term Stability (10 Days)
The long term stability/reproducibility could be affected
by drifts in the instrumental parameters (voltages, trans-
missions, mass-scale-calibration). To estimate this effect,
we compare two calibrations performed at the beginning
and end of the measurement campaign.
The calibration performed on 19.10.2009 was a full
calibration, while the calibration on 30.10.2009 consisted
only of a quick two-point calibration during the course
33 Methanol 35 corrected - Pure H2S 35 H2S + Methanol isot.
Time (min)
Sig
nal (
ncps
)
10 000
1 000
100
10
1
0 50 100 150 200 250 300 350
Figure 4
The measurement of H2S is affected by the methanol signal. The isotopic contribution of the methanol signal atm/z 33 (circles) has been
subtracted from the signal at m/z 35 (triangles) to obtain the pure H2S signal (squares).
TABLE 3
Natural isotopic distribution of protonated methanol (CH4OH+)
m/z (Th) %
33.0 100
34.0 1.12
35.0 0.207
TABLE 4
Mean concentration and standard deviation of HCN in a calibration
measurement. The value in the first cell represents the offset value
at m/z 28 Th
HCN (ppbv) Rel. standard deviation*
0.6 ± 0.5
553 ± 5.0 0.9%
1 504 ± 13.5 0.9%
* Standard deviation over 20 measurements (� 50 minutes).
J. Herbig et al. / Real-Time Monitoring of Trace Gas Concentrations in Syngas 369
of a setup test. Nevertheless, we find excellent agreement
(<3%) between the results, which demonstrates the sta-
bility of the instrumental parameters over this range
(see Tab. 5). The higher deviation for acetonitrile is
attributed to surface effects and insufficient equilibration
in this quick calibration.
2.5 Monitoring Example
Figure 5 shows the measured methanol concentration
for the different streams. This nicely exemplifies several
advantages of the applied method. The concentrations
of stream 1 and 2 are significantly different from the con-
centrations in stream 3 and 4. We observe a general trend
towards higher concentrations downstream of stream 2,
indicating that methanol is formed in these process steps.
Nevertheless, the trend over time is similar for all, which
is not surprising, since they resemble different steps in
the same process.
The sudden changes, as are observed on Oct. 22nd
and Oct 24th, could be attributed to intentional
manipulation of the process, which seem to affect the
formation of methanol. This exemplifies the valuable
information that can be gained from this continuous
monitoring.
2.6 Filter Break-Through
Between the sampling points of stream 1 and stream 2,
the process gas passes through a filter. As can be seen
in Figure 6, we initially observe a working filter, which
efficiently reduces the concentration of the measured
sulfur compound. After Oct 23rd, we observe the
break-through of the filter for this compound. The data
has been normalized to the overall maximum. Measure-
ments at stream 3 and 4 (further downstream of the pro-
cess and not shown in Fig. 6) were below the determined
method-LOD, of 1.3 ppb. Even after the change over
several orders of magnitude in stream 2, we did not
observe elevated concentrations in stream 3 or 4, which
demonstrates that we do not have cross-talk between
the streams.
TABLE 5
Comparison between two calibrations
Compound m/z (Th) Sensitivity 19.10.09
(ncps/ppb)
Sensitivity 30.10.09
(ncps/ppb)
Rel. diff.
Acetonitrile 42.0 20.4 19.8 �2.9%
Acetone 59.0 19.1 19.2 0.4%
Benzene 79.0 10.1 9.95 0.4%
100 000
10 000
1 000
100
Con
cent
ratio
n (p
pbv)
2009
-10-
20
2009
-10-
21
2009
-10-
22
2009
-10-
24
2009
-10-
26
2009
-10-
28
2009
-10-
29
2009
-10-
27
2009
-10-
25
2009
-10-
23
stream 3stream 4
stream 2stream 1
Figure 5
Monitoring of methanol at different gas streams over the
course of 8 days.
100%90%80%70%60%50%40%30%
0%10%20%
2009
-10-
20
2009
-10-
23
2009
-10-
28
2009
-10-
29
2009
-10-
27
2009
-10-
26
2009
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25
2009
-10-
24
2009
-10-
22
2009
-10-
21
stream 1stream 2
Figure 6
A filter break-through can be observed after the 2009-10-22
for stream 2 while monitoring the concentration of a sulfur
compound. Data (y-axis) are normalized to the observed
maximum.
370 Oil & Gas Science and Technology – Rev. IFP Energies nouvelles, Vol. 69 (2014), No. 2
CONCLUSION
In this study, we have employed PTR-MS for the real-
time monitoring of trace compounds in an industrial
Fischer-Tropsch process. With minimal modifications,
a PTR-MS system could be used to measure trace con-
centrations of several organic, inorganic and organome-
tallic compounds in syngas. The use of a multiport valve
allowed for the multiplexed measurement of trace
concentrations at several process steps.
We have thoroughly characterized our system. We
found that the stability of the measurement was on the
order of 1%, which is more than sufficient. The long
term stability (reproducibility) was found to be better
than 3%.We found that the LOD for the presented com-
pounds are around or below 1 ppb. The limitations in
this case were imposed by the experimental setup, since
the LOD imposed by the instrument were found to be
much lower. Based on the experience gathered in this
project, we have been able to compose an optimized
setup for this application. This optional box can be con-
nected to the PTR-MS and includes a multiport valve
and SilconertTM2000 treated mass flow controllers,
which allow for a fully automated measurement. This
system will be a subject of future publications.
Here, we could already show examples, which demon-
strate the added value that can be gained by the informa-
tion from real-time monitoring in such a process. This
can be used for safety to detect the formation of toxic
compounds. The break through of a filter can easily be
detected in its onset, which could be used to protect sen-
sitive parts in the process, like the poisoning of a cata-
lyst. We have also exemplified, that manipulating the
process, like changing process parameters, directly
reflects in the formation or elimination of volatiles. This
information can be used to gain a much deeper under-
standing of the process and its various steps. Ultimately
this could open the door to a new efficiency in process
optimization.
ACKNOWLEDGMENTS
The authors would like to thank Martin Madeira and
the site operators for their kind cooperation during the
experiments.
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Manuscript accepted in November 2012
Published online in August 2013
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372 Oil & Gas Science and Technology – Rev. IFP Energies nouvelles, Vol. 69 (2014), No. 2